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0000000033 21S 4SWS VI Applied Multi-Messenger Astronomy 2: Statistical and Machine Learning Methods in Particle and Astrophysics   Hilfe Logo

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Applied Multi-Messenger Astronomy 2: Statistical and Machine Learning Methods in Particle and Astrophysics 
0000000033
lecture with integrated exercises
4
Summer semester 2021
Chair of Experimental Physics with Cosmic Particles (Prof. Resconi)
(Contact information)
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Angaben zur Abhaltung
Program of the course:
MM Astronomy, part 1 (Cosmic rays, Gamma rays)
MM Astronomy, part 2 (Gravitational waves, neutrino astronomy) - Open questions in the field MM Astronomy
Statistical data analysis: principles
Statistical data analysis, frequentists / Bayesian parameter estimation, hypothesis tests
Introduction to artificial intelligence and common statistical methods between MMA and AI
Straight cuts and machine learning
Application on real data: the diffuse flux measurement in IceCube
Point source sources candidates, AGN, blazars, and others
Point source sources methods in gamma-ray and neutrino astronomy
Modern machine learning methods, neural networks (CNN, boosted decision trees, etc)
MMA meets NN: Applications on real data from IceCube (energy resolution and topological event classification)
Python
Learn and apply methods in particle and astrophysics data analysis and machine learning.
  • German
  • English
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Für die Anmeldung zur Teilnahme müssen Sie sich in TUMonline als Studierende*r identifizieren.
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